For National Doughnut Day I recreated my "RI Dunkin Map" for all of the Northeast states. The maps contain 1 to 5 mile buffer rings around every dunkin and layers it over the given state. This time, rather then using QGIS which is a bit of a lengthy process. I chose to automate 90% of the work with Python, the processing is done with GeoPandas and Shapely, while the mapping with Matplotlib.

An interactive graph showing 2020 COVID-19 pandemic deaths along side the top 15 causes of death in 2018. You can select a state & which binned range you want for 2018 deaths. "Most Recent" is the most recent COVID data vs. top 5 2018 leading causes of death for that month. "Prior Full Month" rolls the COVID data back the the prior full month comparing it to the top 5 causes of death up to that given month. "2018 Full Year" compares the most recent COVID data to the leading 15 causes of death for all of 2018.

4 graphs showing the 7-day moving averages and the last 14-day trends for COVID-19 testing and hospital data in Rhode Island. Charts made with data from RIDOH revised trend sheet, created using Python matplotlib.

This graph shows the change in cases and fatalities between the 2 most recent dates of data provided by RI Department of Health. The top section is nursing homes and the separation at the bottom is assisted livings. RIDOH provides a range for cases/fatalities to the left, you can choose the low or high range, or an average of the 2 numbers and if you wish to view cases or fatalities. Hover over the points for exact numbers and more detail. If you see a single dot with +0 next to it, that means the data was the exact same as the prior week. A single dot with +4, or +9 means the nursing home had no data the prior week.

A map showing COVID-19 cases for Rhode Island by zip code. The map shows the rate per 10,000 people to normalize the data by population. Population data used was Census 2010 ZCTA Population. RIDOH surpressed values under 5, so those are shown as 0.

Manually updating Tableau dashboards isn't exactly hard. First you open up Tableau, next you open the workbook you wish to update, lastly you click on data in the toolbar, hover the dataset you wish to update and click refresh. Not hard, but doing it daily, can begin to be a pain and feel like a chore. Luckily! Tableau Public has google sheets as a datasource and allows you to keep the dashboard synced with the sheet daily updates. This will show you how to add a few lines to your pandas workflow to upload your dataframe to google sheets automatically.

April and March have seen a decline in arrests and case logs. Data is from https://data.providenceri.gov/ which sadly only provides the past 60 days for arrests and 180 days for case records. Due to the limited ammount of days I chose to show the data by week instead of monthly.